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How do consumers respond to real income shocks?
Chapter 6
How do consumers respond to real income shocks? JPMorgan Chase Institute JPMorgan Chase & Co, Washington, DC, United States
1. Introduction How much do peaks and troughs in income feed through to consumers’ economic welfare? In its most stylized form, the permanent income hypothesis (PIH) posits that a family’s current consumption and its planned future consumption depend only on their preferences and expectations about prices and lifetime income, but not on how that income is structured over time. An empirical implication of this hypothesis is that a family’s consumption changes in response to news about a permanent change to income (for example, a lasting change to tax policy), but not in response to transitory or predictable fluctuations (for example, the arrival of a tax refund check). Economic data contain some patterns that are broadly consistent with the spirit of PIH, especially over decades-long time scales and landmark purchases (Hall, 1978; Bernanke, 1984). However, evidence has shown that transitory and predictable fluctuations in income do in fact drive changes in consumption, which contradicts the most stylized representation of PIH.1 The JPMorgan Chase Institute has assembled high-frequency, finely categorized data on income and expenditure of millions of households. Drawing on these data, we describe empirical linkages between expenditures on the one hand and several specific fluctuations in income and prices on the other. The types of fluctuations we have analyzed vary in terms of their levels of predictability and their expected durations (Fig. 6.1). (See Fig. 6.13 in the Appendix for a detailed description of the sampling criteria and data asset in each study.) Consistent with the large body of evidence indicating limitations to consumption smoothing, we observe spending responses even to changes that were
1. For reviews of the extensive empirical literature, see Browning and Lusardi (1996) and Chapters 8e10 of Jappelli and Pistaferri (2017). Handbook of US Consumer Economics. https://doi.org/10.1016/B978-0-12-813524-2.00006-8 Copyright © 2019 Elsevier Inc. All rights reserved.
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Event Arrival of tax refund
Income or price Income
Cash flow impact Positive
Predictability & duration Highly predictable, one-time cash infusion Job loss may be predictable; final unemployment insurance payment is highly predictable Largely unpredictable, duration difficult to forecast
Observed expenditure impact Dramatic increase in spending on healthcare (and nonhealthcare) services Significant decrease in spending at unemployment, and again in the month after the final unemployment insurance payment.
Job loss
Income
Negative
Gas price decline
Price
Positive
Mortgage resets
Price
Positive
Highly predictable, duration of at least one year
Small increase in spending on non-durables upon notification of adjustment, then larger increase when rates adjust
Income fluctuations around mortgage default
Income
Negative
Unknown predictability of income fluctuation; income fluctuation is temporary
People stop paying their mortgage when their income drops temporarily
Dramatic increase in spending on non-durables
FIGURE 6.1 We observe spending responses even to income and price changes that were predictable, that were likely to have no impact on an intertemporal budget constraint, and that have known limited duration.
predictable, likely had no impact on their lifetime income, and had known limited duration. We are able to go beyond confirming the general principle that consumption smoothing is limited because we observe each of the individual transactions that add up to most of each household’s income and expenditure. Using this more granular and fuller view, we are able to describe two underdocumented dimensions of the relationship between cash flow and consumption. First, we are able to document leads and lags in spending responses, which allows us to identify how much spending changes with the arrival of information about a cash flow event and how much with the arrival of the cash itself. For example, do consumers misperceive their permanent income and therefore react to news about the size of a tax refund that should not have been surprising? Or, does cash itself matter more than even very specific information that it will arrive? Second, we are able to characterize not only changes in total spending but also changes in specific categories of spending, including those which consumers would likely prefer to time according to needs rather than cash flow. For example, we can distinguish consumption of nondurables from spending on durables and services, such as healthcare. To hone in on these timing dynamics, we analyze debit and credit card spending at the time of purchase, rather than when the credit card bill is paid. Furthermore, in the case of healthcare spending, we distinguish between in-person card purchases likely made at the point of consumption versus remote or online purchases that could represent bills paid for services received in the past.
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Taken together, our findings across multiple studies indicate consumption patterns that violate not only the most stylized representation of PIH but also contemporary adaptations of dynamic models with perfectly forward-looking consumers. These findings require a frank acknowledgment of the fact that frameworks that rely on rational expectations and intertemporal optimization are inherently limited in terms of their usefulness for understanding consumer behavior or designing optimal policies and financial product offerings. After discussing these studies in more detail, we conclude with a discussion of the implications of these limitations.
2. JPMCI research on consumer spending responses to income and price changes 2.1 Healthcare spending and tax refunds We constructed an event study around the arrival of tax refunds to show that consumer out-of-pocket spending on healthcare is significantly affected by cash flow dynamics (Farrell et al., 2018a). Tax refunds are a significant cash flow event for many households. In 2016, 73% of tax filers received a tax refund, with an average refund of $2860 (Internal Revenue Service, 2017). Families learn the size of their tax refund when they file, although they likely have a good idea of how much to expect even sooner than thatdas shown in Fig. 6.2 (drawn from Farrell et al., 2018b), filers in any age or income group who are owed larger refunds are more likely to receive their refunds earlier in the season, which likely reflects the fact that many filers have an idea of the likely size of their refunds and those who are expecting larger refunds have incentive to submit their returns earlier. Although filers can control the timing of their refund on the scale of weeks or months by filing earlier or later in the season, once they have filed they cannot control or predict exactly when they will receive the funds. Tax refunds, therefore, represent significant positive cash flow that is highly predictable in its size but unpredictable in its timing.
FIGURE 6.2 Even within a demographic group, tax filers who are owed larger refunds file sooner.
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We focus on the healthcare spending response to the arrival of the tax refund, as this is a category of spending where the timing of spending is more likely to be tied to a person’s physical health, and which consumers would likely prefer to time according to their health needs rather than cash flow. Therefore, for healthcare, more than for other spending categories, we can draw potential welfare implications from evidence that families’ cash flow patterns drive them to alter consumption that would have been feasible given their permanent income. We observe that healthcare spending responded sharply to the arrival of the cash, as opposed to the arrival of information. Average healthcare spending was 60% higher in the week starting with the tax refund than in an average week over the 6 months prior, and it remained elevated by 20% for 75 days (Fig. 6.3, drawn from Farrell et al., 2018b). Moreover, 62% of the additional healthcare spending was done in person at healthcare service providers so that it represented deferred care. Almost all of the remaining 38% represented deferred bill payment and a negligible portion representing healthcare goods, such as drugs, which could be stockpiled. Two pieces of evidence allow us to be confident that the cash infusion played a key role in enabling additional healthcare spending (Farrell et al., 2018a). First, the increase in healthcare spending the week after the arrival of the tax refund was entirely attributable to an increase in spending on debit cards (83% increase) and electronic payments (56% increase). There was no increase in credit card spending. Second, the response was 20 times larger for families with the lowest average daily balances in their checking accounts over the year before the refund ($536 or less, the bottom quintile) than those with the highest ($3577 or more, top quintile). We also see evidence that the cash infusion had welfare implications. As evident in Fig. 6.4, those who increased their healthcare spending to a larger
FIGURE 6.3 Out-of-pocket healthcare spending responds immediately to the receipt of a tax refund.
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FIGURE 6.4 Tax filers’ healthcare spending response to their refund is foreshadowed by their filing behavior.
extent after receiving their refund tended to have filed earlier in the season. These early filers also devoted a larger fraction of their healthcare spending response to deferred care (Farrell et al., 2018b). This indicates that people are evidently motivated to file their taxes earlier, not only to receive the cash infusion earlier but also because they prefer to consume healthcare services earlier. Families could, in principle, have received these same healthcare services even earlier, for example, by reprogramming their tax withholding or by spending out of savings and then replenishing with the refund or by borrowing against the anticipated refund. It is implausible that most of these families should be so credit or savings constrained that these are not feasible options for them. Taken together, our results highlight the extent to which families’ consumption of healthcare services is sensitive to a predictable cash infusion even when it has no impact on permanent income and also provide evidence that families would prefer to minimize the impact that cash flow has when they consume healthcare services.
2.2 Consumer spending around job loss and the expiration of unemployment insurance benefits We constructed an event study around the direct deposit of a first unemployment insurance (UI) benefit for 160,000 account holders who received up to 6 months of benefits starting in 2014 (Farrell et al., 2016). A spell of unemployment which qualifies for UI benefits is likely to have been unanticipated in terms of both timing and duration.2 Furthermore, as shown in the green line in
2. To qualify for UI benefits, a job separation must meet specific conditions, including that it must be involuntary (for example, a layoff or a company restructuring).
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FIGURE 6.5 Spending and income both drop sharply at the onset of a spell of unemployment.
Fig. 6.5, such a spell tends to entail a decline in income lasting at least 2 years. This implies that the job separation itself may have brought news of a decline in permanent income. Therefore, that fact that spending declines almost immediately with the onset of an unemployment spell (as shown in the blue line in Fig. 6.5) would be consistent even with the most stylized representation of PIH. Leveraging the granular view of spending afforded by transaction-level data, we can characterize the decline in spending more thoroughly. The sharpest drops are in spending on flights and hotels, restaurant and entertainment, retail, and transport. Each of these categories declines by 9%e11%. Some of this decline likely reflects a decreased need for workrelated spending. When someone stops working, they no longer need to pay every day for expenses such as gas to get to work and buying food at the cafeteria. We also see modest declines in spending on groceries (4%) and utilities (2%). Out-of-pocket medical expenses actually increase by 4%, perhaps in part reflecting the impact of losing employer-provided health insurance. These adjustments at the start of a spell of unemployment fit neatly with almost any hypothesis about consumer behavior that has rational expectations and intertemporal optimization at its core because it is entirely plausible that the beginning of a spell of unemployment might change a family’s beliefs about its lifetime income. However, UI policies in every state explicitly lay out the maximum duration of benefits. For those who remain unemployed for longer than that duration, the end of unemployment benefits represents a perfectly predictable drop in income. However, as shown in the blue lines in Fig. 6.6, spending drops sharply in response to this dropda pattern that is
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FIGURE 6.6 Among the long-term unemployed, spending drops sharply again at the exhaustion of UI benefits, even though that event is perfectly predictable.
impossible to reconcile with almost any rational expectations model of consumer behavior.3 Furthermore, as shown in Fig. 6.7, the consumer welfare impacts of this second spending adjustment are likely even more severe than the first. While spending on groceries, utilities, and debt service was not sharply impacted by the beginning of a spell of unemployment even among the long-term unemployed, spending in these categories drops sharply with exhaustion of UI benefits.4 Families would almost certainly prefer smoother spending patterns for nondurables like these. Cuts to payments on credit cards (17%), auto loans (9%), and mortgages (6%) when UI benefits run out are more modest that cuts to student loan payments (27%). One possible explanation is that the consequences of mortgage and auto delinquency (repossession) and credit card delinquency (loss of a liquid buffer) are more severe than the consequences of student loan delinquency. In addition, income-based repayment policies may allow people in some states to suspend or reduce their student loan payments when their income drops.
3. One way to make this pattern fit a rational expectations hypothesis might be to conjecture that these families would be gradually learning how hard it will be to find a new job, and so they revise their expectations about their intertemporal budget constraint downward a second time as they learn, dropping spending again. However, comparing the dotted blue to the solid blue line in Fig. 6.7 calls that conjecture into serious question; in states where unemployment benefits run out sooner, spending drops sooner. There is no plausible explanation for why families would suddenly revise their expectations in such close coincidence with their states’ policies regarding the end of the benefit period. 4. See also Figs. 3 and 6 of Ganong and Noel (2017).
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FIGURE 6.7 The second spending adjustment among the long-term unemployed (in response to the exhaustion of UI benefits) involves deep cuts on nondurables and significant cuts to debt servicing.
These patterns reflect important limitations to any understanding of consumer behavior that is built on a framework of rational expectations. As families can foresee the drop in income caused by the exhaustion of UI benefits by tracking their benefits payments on a calendar, any model built on rational expectations and intertemporal optimization would imply that as the date of exhaustion draws nearer, they will make more and deeper spending adjustments in preparation. On the other hand, as illustrated by the fact that the blue lines are significantly smoother than the green lines in Figs. 6.6 and 6.7 (and as shown in Ganong and Noel, 2018), families on average do manage to smooth over the income changes to some extentdthe patterns are not consistent with any single canonical stylized modeldneither the most stylized representation of PIH, the standard “buffer stock” model (Carroll, 1997), nor a stylized model of families living hand to mouth. One important possibility discussed by Ganong and Noel (2018) is that the average patterns indicated here might reflect heterogeneity among families, with distinct groups of families behaving in concordance with each of these different models.
2.3 Consumer spending and the decline of gas prices between 2014 and 2015 Gas prices were 25% lower in 2015 than in the prior year ($2.60 in 2015 compared with $3.47 in 2014).5 This price change was unpredictable in terms 5. Gas prices fell sharply in the fourth quarter of 2014 and remained low for most of 2015 despite some fluctuations. Starting at a peak monthly price of $3.77 in June 2014, prices fell precipitously and continuously to a trough of $2.21 in January 2015 (Fig. 6.1). Using a year-over-year comparison to account for seasonality, November 2014 was the first month in which gas prices were lower than in the prior year. National average gas prices subsequently rose in the first half of 2015 to $2.89 in June, and prices reached a high of $4 in California.
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of both timing and duration. If families believed the change in prices was to be relatively short-lived, then we should see no material change in consumption behavior. If, on the other hand, families believed the change represented a structural shift, such that prices would remain low over the long run, then this may have led them to revise their beliefs about their total lifetime purchasing power. In that case, an increase in total spending would be consistent with almost any rational expectations model of consumer behavior. However, given that fuel represents just 5% of the average family’s total spending, the total lifetime purchasing power impact of even such a steep drop in fuel prices is relatively small. We examined a sample of 1 million core Chase customers to ascertain the magnitude of savings households experienced from lower gas prices, and whether and on what they spent these windfall gains; our findings are summarized in Fig. 6.8 (Farrell and Greig, 2016). We estimated that the 25% drop in gas prices generated a potential savings of $632 for middle-income households, of which only 42% went to durables purchases or savings. About three-fifths of the remaining 58% went to spending on nondurable goods and services other than fuel such as restaurants, retail, and groceries, while the rest went toward increased consumption of fuel. Some of this increase in fuel spending represented substitution away from other transit options.
FIGURE 6.8 Consumers spent 58% of their potential savings from lower fuel prices on nondurables.
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If the price change is certain to be short-lived, then a family seeking to smooth its consumption would save almost all of the dollars freed up by the price change in preparation for when prices returned to normal. If the price change is certain to be permanent, then they would be able to spend all of it. So, “should” families have spent 58% of these freed up dollars on nondurables? As it turns out, average fuel prices in 2017 were still at similar levels to 2015, suggesting that so far the decision to spend almost three-fifths of the freed up dollars may reflect about the right amount of optimism regarding the duration of the price change in this particular case. However, as we illustrate in the next example, consumer responses to price changes do not always work out so well.
2.4 Consumption, investment, and mortgage resets People increase their spending when they receive news that their adjustablerate mortgage (ARM) payment will reset downward, but then increase again when the reset actually starts. We examined a sample of 4321 deidentified US homeowners with a 5/1 ARM originated between April 2005 and December 2007, which reset to a lower interest rate between April 2010 and December 2012 (Farrell et al., 2017). Under Federal mortgage servicing rules, each of these homeowners was notified in advance of the adjustment what their new interest rate and monthly payment would be. When they received this information, they were also told that the new payment amount would last at least a year. We examined how these homeowners changed their credit card spending and revolving balances in response to the news of this predictable price change of known duration, as well as in response to the price change itself. Fig. 6.9 shows the pace of credit card spending around the change in these homeowners’ mortgage payments. Average spending increased sharply about 8 months before the rate reset and again about 2 months prior. This is consistent with a family responding to news that its lifetime purchasing power has increased slightly, as the cost of its housing will be lower for at least a year. Spending increased sharply again in the month when the rate actually reset, even though this second change was perfectly predictable. Looking across categories of spending (Fig. 6.10), we observed variation in the staging of the spending response. Spending on auto repair, transportation, home improvement, and services was highly responsive to news of the coming rate change and did not change much when rates actually changed, whereas spending on staples, healthcare, retail, and leisure was more responsive to the predictable change in payment amount than to news. With the notable exception of healthcare, it is likely that spending changes in response to the news had broader consequences for families’ well-being than changes in response to the actual cash flow event (for example, delaying an auto repair likely has broader impacts on a family’s ability to meet its needs than delaying a retail purchase).
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FIGURE 6.9 Credit card spending among consumers increased in response to information about a decline in their monthly mortgage payment and then increased again when the lower payment actually took effect.
FIGURE 6.10 Homeowners responded to news about a rate reset by increasing spending in some categories and to the rate reset itself by increasing spending in others.
152 Handbook of US Consumer Economics Cumulative average change in income, spending, and revolving balance
$9,327
$9,500
$928
$9,000
Financed on credit card
$8,500
$363 $565
Excess spending Excess financing
$8,000 $7,500
$8,399
$7,000
$8,964 $6,500
Likely paid for out of income
$6,000 $1,000 $500 $0 Income increase from mortgage reset
Total spending increase
Revolving balance increase
FIGURE 6.11 The credit card spending response to a mortgage rate reset exceeded the total decline in housing costs by 4%, but their credit card debt increased even more than was necessary to cover the excess.
Overall, as shown by the left column in Fig. 6.11, lower payments freed up about $8964 per year for the average homeowner over the year following the rate reset. However, as shown by the middle column, the annual increase in credit card spending exceeded those freed up dollars by 4%. Any understanding of consumer behavior that is built around a rational expectations framework would indicate that families should only increase consumption by more than the reduction in payments if they believed that their payments would continue to decline into the future. However, as the right column in Fig. 6.11 illustrates, the increase in revolving credit card balances was 2.6 times larger than the excess spending response. Simply by reallocating the dollars freed up by lower housing costs, these homeowners could have had exactly the same spending response with considerably lower credit card debt. Regardless of how optimistic they might have been about future interest rate adjustments, the average homeowner overreacted to the price change, increasing their consumption more than their long-run income increased. This is especially clear because for the vast majority of homeowners, any increase in expected lifetime purchasing power resulting from lower housing costs was only offsetting a decline in lifetime wealth from a lower home values. Median home values had declined by $84,000 in the period between when these mortgages were originated and when the payment resets occurred. These patterns suggest that the impact of a change in monthly housing payments on monthly nonhousing consumption exceeds their impact on lifetime purchasing power. These implications are further supported by later research on the impacts of mortgage modifications that were offered by the Home Affordable Modification Program (HAMP) in the wake of the Great Recession (Ganong and Noel, 2017; Farrell et al., 2017b). That research
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indicated that modifications which included a principal write-down were no more effective at reducing defaults than those that did not. These two classes of modifications had similar impacts on monthly payments, but very different impacts on total lifetime housing costs. Principal reduction that did not result in an incremental reduction in monthly mortgage payment had little impact on immediate cash flows and therefore little impact on default. Principal reduction also had no impact on consumption. Those who received payment and principal reduction spent no more than those who received only payment reductions, despite the wealth effect of principal reduction.
2.5 Income fluctuations around mortgage defaults The affordability of a mortgage is traditionally gauged by long-run metrics like the ratio of steady-state debt payments to expected average monthly income over the period of the loan.6 However, short-run fluctuations in income and prices are also of primary importance. This is evident from the fact that not only do people increase their spending when their mortgage payment drops but also people stop paying their mortgage when their income drops temporarily (Farrell et al., 2017b). Mortgage default closely followed a sharp drop in income, and mortgage payments recovered as income recovered (Fig. 6.12). This pattern held regardless of where homeowners fell in terms of traditional affordability metrics (e.g., payment to income ratio) or the amount of equity they had in their homes (e.g., “abovewater” borrowers, who have positive home equity in that they owe less on their home than their home is worth vs. “underwater” borrowers, who have negative home equity in that owe more on their home than their home is worth). The income drop before default was similar across all of these groups, suggesting that it was an income shock rather than a high payment burden or negative home equity that triggered default. Payment-focused mortgage debt reduction was equally effective at slowing default than principal-focused mortgage debt reduction precisely because it targeted relief to a household’s cash flow rather than to their balance sheet.
3. Conclusion Leveraging high-frequency, finely categorized data on income and expenditure of millions of households, we have described how expenditure responds to four specific changes in prices and income with varying degrees of impact and predictability. Consistent with the large body of evidence indicating limitations to consumption smoothing, we observe spending responses even to 6. For example, for a lender to get all of the regulatory and financial advantages associated with Qualified Mortgages, they must confirm that all of the borrower’s steady-state debt payments amount to no more than 43% of their expected average monthly income.
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FIGURE 6.12 On average, a substantial negative income shock preceded default for both belowmedian and above-median mortgage payment-to-income borrowers and both abovewater and underwater borrowers.
changes that likely had no impact on lifetime purchasing power. We are able to go beyond confirming the general principle that consumption smoothing is limited, showing that expenditure responds to both information about a future price or income change and the (temporary) change in purchasing power when it actually occurs. Furthermore, we find that expenditures for which timing is likely to have welfare implications, such has healthcare, adjust upward in response to positive news and downward in response to negative events. These findings indicate consumption patterns that violate not only the most stylized representation of the PIH but also contemporary adaptations built on a rational expectations framework. At the same time, the evidence indicates that consumers do not simply live hand to mouth; in two cases (mortgage rate resets and job separations), we observed consumers reacting to news about changes to lifetime purchasing power even before those changes took effect. This suggests that consumers do manage their cash flow and take the future into account when making consumption decisions. Understanding the nature
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and extent of limitations to consumption smoothing is crucial for the design of optimal policies and financial product offerings. We offer a few specific examples below. Consumers need tools that make it easier to budget, save, and adjust. In response to perfectly forecastable changes in their long-run purchasing power, we have observed consumers underreacting to the prospect of a decline (expiration of UI benefits) and overreacting to an improvement (mortgage rate resets). The cognitive burden associated with reprogramming a family’s spending, saving, and borrowing when circumstances change is almost never taken into account when consumption behavior is analyzed through the lens of a rational expectations framework. Many families would likely benefit from financial tools that help with the complicated process of making these adjustments. These tools would make budgeting more intuitive and make it easier to stick to a plan with “set-it-and-forget-it” (or “reset-it-and-forget-it”) functionality. For example, if more UI recipients were given a tool to preallocate a fraction of their benefits to a sidecar account to provision for the day they exhaust their eligibility, we may not have observed the sharp declines in grocery spending. In addition, many might benefit from simpler financial offerings that make it less likely these kinds of adjustments will be necessary in the first place. Families need more shock absorbers, especially for healthcare. In three of our four examplesdtax refunds, mortgage resets, and UI expirationdwe observe predictable changes in cash flow having sharp and significant impacts on many important expenditure categories, including healthcare. Our research has also consistently shown that these impacts are smaller for families with higher levels of cash savings. It should be easier for families to save for emergencies. During tax filing season, families appear to look to their refunds to access a large cash infusion, but emergencies happen throughout the year. Health savings accounts, medical reimbursement accounts, and other taxadvantaged savings vehicles make it possible for families to provision specifically for health emergencies, but they carry complicated rules and impose significant penalties if the family ends up needing the cash for a nonhealth emergency. Policies and tools that enable families to provision more effectively might include many of the useful features of these existing tools, but without these rigidities. Means testing for social safety nets should focus on income levels, not just asset levels. Asset tests penalize families for building up reserves necessary to smooth consumption. The impact of even short-run changes in purchasing power on current consumption exceeds their impact on lifetime purchasing power. Families who have provisioned for emergencies should not be forced to exhaust those savings before they can get help with managing the impacts of volatility. Underwriting standards should account for income volatility, not merely steady-state income. Static affordability metrics like the ratio of steady-state
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debt payments to expected average monthly income are not good predictors of default risk, in part because even transitory disruptions to income have larger impacts than would be predicted by any rational expectations model. Dynamic approaches which account for cash flow dynamics and forecast income and expense risk should be more widely integrated into underwriting practices. There is value in tracking even transitory fluctuations in income and spending. For most economic monitoring applications, income and spending measures are adjusted to smooth out predictable variation (for example, seasonal variation). These adjustments are implicitly motivated by assumptions about the welfare impacts of predictable fluctuations. The findings we report here suggest that those assumptions should be revisited for some applications. In general, the justification for smoothing should be more explicit and question-specific. We need a better theoretical framework for understanding consumer behavior. Many of the patterns we report here, and other patterns that have been consistently established in academic studies, are inconsistent with the workhorse rational expectations framework that has helped to organize thinking around consumer behavior. Some scholars have attributed a lack of consumption smoothing to liquidity constraints, but these models would fail to explain the excess consumption we observed, for example, in the consumption response leading up to and after mortgage resets (Carroll, 1997; Kaplan et al., 2014). Behavioral scientists have offered alternative frameworks that relax assumptions of rational expectations and intertemporal optimization, but none are as comprehensive or broadly applicable (for example, Madrian and Shea, 2001; DellaVigna and Malmendier, 2006; Olafsson and Pagel, 2018). Key features of some of these models include more robust conceptions of how consumers form and update expectations about the future, and how they approach intertemporal trade-offs. Many of these features have been used to extend the constrained optimization paradigm that lies at the foundation of modern microeconomics, but these extensions are often very applicationspecific. The design of optimal public policies and financial product offerings would benefit immeasurably from a coherent model of consumer behavior that captures these nuances. Products and policies designed to serve perfectly forward-looking consumers, or entirely myopic hand-to-mouth consumers, will always be of limited use to most real-world consumers.
Appendix FIGURE 6.13. Description of sampling criteria and data asset for each study.
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Event
Sampling criteria and data asset
Arrival of tax refund (Farrell et al., 2018a, 2018b) Figs. 6.2e6.4
This report draws on the JPMC Institute healthcare out-of-pocket spending Panel (JPMCI HOSP) data asset and examines how healthcare payments vary in the days and weeks around when account holders receive their tax refunds. We analyze average outof-pocket healthcare expenditure on over a dozen categories of healthcare goods and services for each day in the 100 days before and after a tax refund payment, for 1.2 million checking account holders in the JMPCI HOSP who received a tax refund between 2014 and 2016. The JPMCI HOSP data asset was constructed using a sample of deidentified core Chase customers for whom we observe financial attributes, including out-of-pocket healthcare spending between 2013 and 2016. For the purposes of our research, the unit of analysis was the primary account holder. We focused on accounts held by adults aged 18e64, as adults 65 and older were more likely to make payments using paper checks, which we could not categorize. To provide better visibility into income and spending, we selected accounts which met the following criteria: 1. Had at least five checking account outflows each month 2. Had at least $5000 in take-home income each year 3. Used paper checks, cash, and non-Chase credit cards for less than 50% of their total spending. The JPMCI HOSP data asset includes customers who resided within the 23 states in which JPMorgan Chase has a retail branch presence. We reweighted our population to reflect the joint age and income distribution among the 18e64-year-old population within each state.
Job loss (Farrell et al., 2016) Figs. 6.5e6.7
From a universe of 28 million Chase checking account holders, this report assembled an anonymized sample of 160,000 families across 18 states who met the following five criteria. 1. Received direct deposit of their first unemployment insurance (UI) check after December 2013 and their last UI check before June 2015 2. Received UI for six or fewer contiguous months 3. Experienced one spell of receiving UI benefits 4. Live in states that offer 26 weeks of UI benefits. In finding 3, we compare this group to UI recipients in Florida where benefits lasted 16 weeks in 2014 and 14 weeks in 2015 5. Have at least five outflows out of their checking account in the 3 months before and after UI receipt. Among our sample, median earnings among families that received UI benefits was $4,540, roughly comparable with the national median of $5106. Among the families in our sample, we studied income and spending by analyzing inflows and outflows out of the checking account as well as on Chase debit and credit cards. We defined income as all inflows which are not explicitly categorized as transfers from other financial accounts, and we rescaled take-home labor income into pretax dollars. We defined spending to include debit card expenditures, Chase credit card expenditures, consumer debt payments (mortgages, auto loans, non-Chase credit cards, and student loans), bills (e.g., electricity, cable, insurance), and cash withdrawals from the ATM.
Continued
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dcont’d Event
Sampling criteria and data asset
Gas price decline (Farrell and Greig, 2016) Fig. 6.8
For this report we rely on JPMorgan Chase anonymized data on consumer clients who are primary account holders. To avoid double counting of financial activity, all joint accounts are captured under the primary account holder. From a universe of over 28 million anonymized checking account holders, we created a sample of approximately one million debit card holders who meet the following sample criteria: 1. They have a checking account and at least five outflow transactions from their checking account per month between October 2012 and January 2016. 2. They do not hold a gas station specific card. 3. They live in a zip code with at least 140 households in our sample. 4. They live in a metro area with at least 5 zip codes and at least 750 households in our sample.These criteria give us confidence that we are focusing on core Chase clients and have sufficient coverage of the geographic areas in which we assess the impact of low gas prices on spending behavior. These criteria constrain our sample to the 23 states with Chase branch locations. The demographic characteristics of this sample are slightly different from the nation in that the sample overrepresents primary account holders between 25 and 54 years old, men, households in the West, and households with higher incomes compared to the US population.
Mortgage resets (Farrell et al., 2017) Figs. 6.9e6.11
From a universe of over 6 million mortgage customers, we created a sample of 4321 homeowners who met the following criteria: 1. Had one 30-year 5/1 adjustable-rate mortgage (ARM) originated between April 2005 and December 2007 that reset to a lower rate between April 2010 and December 2012 2. Had not modified or refinanced their mortgage before reset 3. Made interest-only or interest plus principal payments To connect the impact of ARM resets to changes in spending, we then filter our sample to include those customers who have a Chase credit card that 4. Was active at least 24 months before the reset date of their ARM and 5. Had a median of at least 10 transactions per month in the 24 month window surrounding the reset date of their ARM. We require the Chase credit card to be active at least 24 months before the reset date of their ARM to eliminate households who opened a credit card just before reset to make a large purchase. We also require a median of at least 10 transactions per month in the 24month window surrounding the ARM reset date to eliminate households whose Chase credit card is not sufficiently active to be representative of their consumption.
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dcont’d Event
Sampling criteria and data asset For our sample, we observe loan amount, term, interest rate, monthly payment, home value estimate, monthly credit card spending, spending by category, revolving balance, and credit limit. We also have access to demographic information such as customer age and annual income. Our sample is not perfectly representative of the typical household with any type of mortgage. Our loan amounts and LTVs are in line with the Federal Housing Finance Agency benchmarks, while our mortgage rates are somewhat lower. The sample also exhibits higher income levels than Survey of Consumer Finance benchmarks. This is partially the result of studying hybrid ARMs. The income of our sample also increases as we screen for credit card holders and sufficient credit card activity.
Income fluctuations around mortgage default (Farrell et al, 2018a,b) Fig. 6.12
For this analysis, we included homeowners who met the following criteria: 1. Chase customers with only one mortgage and a Chase deposit account 2. First default date between October 2013 and October 2014 3. Had monthly mortgage data and an active deposit account for at least 12 months before and 12 months after default. We used this sample of 10,815 mortgages to analyze the correlation between income and default. We then split this sample into aboveand underwater borrowers and analyzed each subsample separately. To examine the correlation between income and default split by above- and below-median premodification mortgage payment to income, we used the sample above but added a restriction that the borrower must have received a modification in the 12 months before or after default. This limitation is necessary because we sourced borrower income from their modification application. This generated a sample of 1807 mortgages.
References Bernanke, B.S., 1984. Permanent income, liquidity, and expenditure on automobiles: evidence from panel data. Quarterly Journal of Economics 99 (3), 586e614. Browning, M., Lusardi, A., 1996. Household saving: micro theories and micro facts. Journal of Economic Literature 34 (4), 1797e1855. Carroll, C.D., 1997. Buffer-stock saving and the life cycle/permanent income hypothesis. Quarterly Journal of Economics 112 (1), 1e55. DellaVigna, S., Malmendier, U., 2006. Paying not to go to the gym. The American Economic Review 96 (3), 694e719. Farrell, D., Greig, F., 2016. The Consumer Response to a Year of Low Gas Prices: Evidence from 1 Million People. JPMorgan Chase Institute.
160 Handbook of US Consumer Economics Farrell, D., Greig, F., Hamoudi, A., 2018a. Deferred Care: How Tax Refunds Enable Healthcare Spending. JPMorgan Chase Institute. Farrell, D., Greig, F., Hamoudi, A., 2018b. Filing Taxes Early, Getting Healthcare Late: Insights from 1.2 Million Households. JPMorgan Chase Institute. Farrell, D., Bhagat, K., Narasiman, V., 2017a. The Consumer Spending Response to Mortgage Resets: Microdata on Monetary Policy. JPMorgan Chase Institute. Farrell, D., Bhagat, K., Ganong, P., Noel, P., 2017b. Mortgage Modifications After the Great Recession: New Evidence and Implications for Policy. JPMorgan Chase Institute. Farrell, D., Ganong, P., Greig, F., Noel, P., 2016. Recovering from Job Loss: The Role of Unemployment Insurance. JPMorgan Chase Institute. Ganong, P., Noel, P., 2017. The Effect of Debt on Default and Consumption: Evidence from Housing Policy in the Great Recession. Mimeo, Harvard University. Available at: https:// scholar.harvard.edu/ganong/publications/effect-debt-default-and-consumption-evidencehousing-policy-great-recession. Ganong, P., Noel, P., 2018. Consumer Spending During Unemployment: Positive and Normative Implications. Mimeo, Harvard University. Available at: https://scholar.harvard.edu/ganong/ publications/how-does-unemployment-affect-consumer-spending-job-market-paper. Gelman, M., Kariv, S., Shapiro, M.D., Silverman, D., Tadelis, S., 2015. How Individuals Smooth Spending: Evidence from the 2013 Government Shutdown Using Account Data. No. w21025. National Bureau of Economic Research. Hall, R.E., 1978. Stochastic implication of the life cycle-permanent income hypothesis: theory and evidence. Journal of Political Economy 86, 971e988. Internal Revenue Service (IRS). Filing Season Statistics for Week Ending, 2016. Internal Revenue Service, 2017. Available at: https://www.irs.gov/newsroom/filing-season-statistics-for-theweek-ending-december-30-2016. Jappelli, T., Pistaferri, L., 2017. The Economics of Consumption: Theory and Evidence. Oxford University Press. Kaplan, G., Violante, G.L., Weidner, J., Spring 2014. The wealthy hand-to-mouth. In: Brookings Papers on Economic Activity. Madrian, B.C., Shea, D.F., 2001. The power of suggestion: inertia in 401(k) participation and savings behavior. Quarterly Journal of Economics 116 (4), 1149e1187. Olafsson, A., Pagel, M., 2018. The liquid hand-to-mouth: evidence from personal finance management software. The Review of Financial Studies 31 (11), 4398.
How do consumers respond to real income shocks? JPMorgan Chase Institute JPMorgan Chase & Co, Washington, DC, United States
1. Introduction How much do peaks and troughs in income feed through to consumers’ economic welfare? In its most stylized form, the permanent income hypothesis (PIH) posits that a family’s current consumption and its planned future consumption depend only on their preferences and expectations about prices and lifetime income, but not on how that income is structured over time. An empirical implication of this hypothesis is that a family’s consumption changes in response to news about a permanent change to income (for example, a lasting change to tax policy), but not in response to transitory or predictable fluctuations (for example, the arrival of a tax refund check). Economic data contain some patterns that are broadly consistent with the spirit of PIH, especially over decades-long time scales and landmark purchases (Hall, 1978; Bernanke, 1984). However, evidence has shown that transitory and predictable fluctuations in income do in fact drive changes in consumption, which contradicts the most stylized representation of PIH.1 The JPMorgan Chase Institute has assembled high-frequency, finely categorized data on income and expenditure of millions of households. Drawing on these data, we describe empirical linkages between expenditures on the one hand and several specific fluctuations in income and prices on the other. The types of fluctuations we have analyzed vary in terms of their levels of predictability and their expected durations (Fig. 6.1). (See Fig. 6.13 in the Appendix for a detailed description of the sampling criteria and data asset in each study.) Consistent with the large body of evidence indicating limitations to consumption smoothing, we observe spending responses even to changes that were
1. For reviews of the extensive empirical literature, see Browning and Lusardi (1996) and Chapters 8e10 of Jappelli and Pistaferri (2017). Handbook of US Consumer Economics. https://doi.org/10.1016/B978-0-12-813524-2.00006-8 Copyright © 2019 Elsevier Inc. All rights reserved.
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Event Arrival of tax refund
Income or price Income
Cash flow impact Positive
Predictability & duration Highly predictable, one-time cash infusion Job loss may be predictable; final unemployment insurance payment is highly predictable Largely unpredictable, duration difficult to forecast
Observed expenditure impact Dramatic increase in spending on healthcare (and nonhealthcare) services Significant decrease in spending at unemployment, and again in the month after the final unemployment insurance payment.
Job loss
Income
Negative
Gas price decline
Price
Positive
Mortgage resets
Price
Positive
Highly predictable, duration of at least one year
Small increase in spending on non-durables upon notification of adjustment, then larger increase when rates adjust
Income fluctuations around mortgage default
Income
Negative
Unknown predictability of income fluctuation; income fluctuation is temporary
People stop paying their mortgage when their income drops temporarily
Dramatic increase in spending on non-durables
FIGURE 6.1 We observe spending responses even to income and price changes that were predictable, that were likely to have no impact on an intertemporal budget constraint, and that have known limited duration.
predictable, likely had no impact on their lifetime income, and had known limited duration. We are able to go beyond confirming the general principle that consumption smoothing is limited because we observe each of the individual transactions that add up to most of each household’s income and expenditure. Using this more granular and fuller view, we are able to describe two underdocumented dimensions of the relationship between cash flow and consumption. First, we are able to document leads and lags in spending responses, which allows us to identify how much spending changes with the arrival of information about a cash flow event and how much with the arrival of the cash itself. For example, do consumers misperceive their permanent income and therefore react to news about the size of a tax refund that should not have been surprising? Or, does cash itself matter more than even very specific information that it will arrive? Second, we are able to characterize not only changes in total spending but also changes in specific categories of spending, including those which consumers would likely prefer to time according to needs rather than cash flow. For example, we can distinguish consumption of nondurables from spending on durables and services, such as healthcare. To hone in on these timing dynamics, we analyze debit and credit card spending at the time of purchase, rather than when the credit card bill is paid. Furthermore, in the case of healthcare spending, we distinguish between in-person card purchases likely made at the point of consumption versus remote or online purchases that could represent bills paid for services received in the past.
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Taken together, our findings across multiple studies indicate consumption patterns that violate not only the most stylized representation of PIH but also contemporary adaptations of dynamic models with perfectly forward-looking consumers. These findings require a frank acknowledgment of the fact that frameworks that rely on rational expectations and intertemporal optimization are inherently limited in terms of their usefulness for understanding consumer behavior or designing optimal policies and financial product offerings. After discussing these studies in more detail, we conclude with a discussion of the implications of these limitations.
2. JPMCI research on consumer spending responses to income and price changes 2.1 Healthcare spending and tax refunds We constructed an event study around the arrival of tax refunds to show that consumer out-of-pocket spending on healthcare is significantly affected by cash flow dynamics (Farrell et al., 2018a). Tax refunds are a significant cash flow event for many households. In 2016, 73% of tax filers received a tax refund, with an average refund of $2860 (Internal Revenue Service, 2017). Families learn the size of their tax refund when they file, although they likely have a good idea of how much to expect even sooner than thatdas shown in Fig. 6.2 (drawn from Farrell et al., 2018b), filers in any age or income group who are owed larger refunds are more likely to receive their refunds earlier in the season, which likely reflects the fact that many filers have an idea of the likely size of their refunds and those who are expecting larger refunds have incentive to submit their returns earlier. Although filers can control the timing of their refund on the scale of weeks or months by filing earlier or later in the season, once they have filed they cannot control or predict exactly when they will receive the funds. Tax refunds, therefore, represent significant positive cash flow that is highly predictable in its size but unpredictable in its timing.
FIGURE 6.2 Even within a demographic group, tax filers who are owed larger refunds file sooner.
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We focus on the healthcare spending response to the arrival of the tax refund, as this is a category of spending where the timing of spending is more likely to be tied to a person’s physical health, and which consumers would likely prefer to time according to their health needs rather than cash flow. Therefore, for healthcare, more than for other spending categories, we can draw potential welfare implications from evidence that families’ cash flow patterns drive them to alter consumption that would have been feasible given their permanent income. We observe that healthcare spending responded sharply to the arrival of the cash, as opposed to the arrival of information. Average healthcare spending was 60% higher in the week starting with the tax refund than in an average week over the 6 months prior, and it remained elevated by 20% for 75 days (Fig. 6.3, drawn from Farrell et al., 2018b). Moreover, 62% of the additional healthcare spending was done in person at healthcare service providers so that it represented deferred care. Almost all of the remaining 38% represented deferred bill payment and a negligible portion representing healthcare goods, such as drugs, which could be stockpiled. Two pieces of evidence allow us to be confident that the cash infusion played a key role in enabling additional healthcare spending (Farrell et al., 2018a). First, the increase in healthcare spending the week after the arrival of the tax refund was entirely attributable to an increase in spending on debit cards (83% increase) and electronic payments (56% increase). There was no increase in credit card spending. Second, the response was 20 times larger for families with the lowest average daily balances in their checking accounts over the year before the refund ($536 or less, the bottom quintile) than those with the highest ($3577 or more, top quintile). We also see evidence that the cash infusion had welfare implications. As evident in Fig. 6.4, those who increased their healthcare spending to a larger
FIGURE 6.3 Out-of-pocket healthcare spending responds immediately to the receipt of a tax refund.
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FIGURE 6.4 Tax filers’ healthcare spending response to their refund is foreshadowed by their filing behavior.
extent after receiving their refund tended to have filed earlier in the season. These early filers also devoted a larger fraction of their healthcare spending response to deferred care (Farrell et al., 2018b). This indicates that people are evidently motivated to file their taxes earlier, not only to receive the cash infusion earlier but also because they prefer to consume healthcare services earlier. Families could, in principle, have received these same healthcare services even earlier, for example, by reprogramming their tax withholding or by spending out of savings and then replenishing with the refund or by borrowing against the anticipated refund. It is implausible that most of these families should be so credit or savings constrained that these are not feasible options for them. Taken together, our results highlight the extent to which families’ consumption of healthcare services is sensitive to a predictable cash infusion even when it has no impact on permanent income and also provide evidence that families would prefer to minimize the impact that cash flow has when they consume healthcare services.
2.2 Consumer spending around job loss and the expiration of unemployment insurance benefits We constructed an event study around the direct deposit of a first unemployment insurance (UI) benefit for 160,000 account holders who received up to 6 months of benefits starting in 2014 (Farrell et al., 2016). A spell of unemployment which qualifies for UI benefits is likely to have been unanticipated in terms of both timing and duration.2 Furthermore, as shown in the green line in
2. To qualify for UI benefits, a job separation must meet specific conditions, including that it must be involuntary (for example, a layoff or a company restructuring).
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FIGURE 6.5 Spending and income both drop sharply at the onset of a spell of unemployment.
Fig. 6.5, such a spell tends to entail a decline in income lasting at least 2 years. This implies that the job separation itself may have brought news of a decline in permanent income. Therefore, that fact that spending declines almost immediately with the onset of an unemployment spell (as shown in the blue line in Fig. 6.5) would be consistent even with the most stylized representation of PIH. Leveraging the granular view of spending afforded by transaction-level data, we can characterize the decline in spending more thoroughly. The sharpest drops are in spending on flights and hotels, restaurant and entertainment, retail, and transport. Each of these categories declines by 9%e11%. Some of this decline likely reflects a decreased need for workrelated spending. When someone stops working, they no longer need to pay every day for expenses such as gas to get to work and buying food at the cafeteria. We also see modest declines in spending on groceries (4%) and utilities (2%). Out-of-pocket medical expenses actually increase by 4%, perhaps in part reflecting the impact of losing employer-provided health insurance. These adjustments at the start of a spell of unemployment fit neatly with almost any hypothesis about consumer behavior that has rational expectations and intertemporal optimization at its core because it is entirely plausible that the beginning of a spell of unemployment might change a family’s beliefs about its lifetime income. However, UI policies in every state explicitly lay out the maximum duration of benefits. For those who remain unemployed for longer than that duration, the end of unemployment benefits represents a perfectly predictable drop in income. However, as shown in the blue lines in Fig. 6.6, spending drops sharply in response to this dropda pattern that is
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FIGURE 6.6 Among the long-term unemployed, spending drops sharply again at the exhaustion of UI benefits, even though that event is perfectly predictable.
impossible to reconcile with almost any rational expectations model of consumer behavior.3 Furthermore, as shown in Fig. 6.7, the consumer welfare impacts of this second spending adjustment are likely even more severe than the first. While spending on groceries, utilities, and debt service was not sharply impacted by the beginning of a spell of unemployment even among the long-term unemployed, spending in these categories drops sharply with exhaustion of UI benefits.4 Families would almost certainly prefer smoother spending patterns for nondurables like these. Cuts to payments on credit cards (17%), auto loans (9%), and mortgages (6%) when UI benefits run out are more modest that cuts to student loan payments (27%). One possible explanation is that the consequences of mortgage and auto delinquency (repossession) and credit card delinquency (loss of a liquid buffer) are more severe than the consequences of student loan delinquency. In addition, income-based repayment policies may allow people in some states to suspend or reduce their student loan payments when their income drops.
3. One way to make this pattern fit a rational expectations hypothesis might be to conjecture that these families would be gradually learning how hard it will be to find a new job, and so they revise their expectations about their intertemporal budget constraint downward a second time as they learn, dropping spending again. However, comparing the dotted blue to the solid blue line in Fig. 6.7 calls that conjecture into serious question; in states where unemployment benefits run out sooner, spending drops sooner. There is no plausible explanation for why families would suddenly revise their expectations in such close coincidence with their states’ policies regarding the end of the benefit period. 4. See also Figs. 3 and 6 of Ganong and Noel (2017).
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FIGURE 6.7 The second spending adjustment among the long-term unemployed (in response to the exhaustion of UI benefits) involves deep cuts on nondurables and significant cuts to debt servicing.
These patterns reflect important limitations to any understanding of consumer behavior that is built on a framework of rational expectations. As families can foresee the drop in income caused by the exhaustion of UI benefits by tracking their benefits payments on a calendar, any model built on rational expectations and intertemporal optimization would imply that as the date of exhaustion draws nearer, they will make more and deeper spending adjustments in preparation. On the other hand, as illustrated by the fact that the blue lines are significantly smoother than the green lines in Figs. 6.6 and 6.7 (and as shown in Ganong and Noel, 2018), families on average do manage to smooth over the income changes to some extentdthe patterns are not consistent with any single canonical stylized modeldneither the most stylized representation of PIH, the standard “buffer stock” model (Carroll, 1997), nor a stylized model of families living hand to mouth. One important possibility discussed by Ganong and Noel (2018) is that the average patterns indicated here might reflect heterogeneity among families, with distinct groups of families behaving in concordance with each of these different models.
2.3 Consumer spending and the decline of gas prices between 2014 and 2015 Gas prices were 25% lower in 2015 than in the prior year ($2.60 in 2015 compared with $3.47 in 2014).5 This price change was unpredictable in terms 5. Gas prices fell sharply in the fourth quarter of 2014 and remained low for most of 2015 despite some fluctuations. Starting at a peak monthly price of $3.77 in June 2014, prices fell precipitously and continuously to a trough of $2.21 in January 2015 (Fig. 6.1). Using a year-over-year comparison to account for seasonality, November 2014 was the first month in which gas prices were lower than in the prior year. National average gas prices subsequently rose in the first half of 2015 to $2.89 in June, and prices reached a high of $4 in California.
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of both timing and duration. If families believed the change in prices was to be relatively short-lived, then we should see no material change in consumption behavior. If, on the other hand, families believed the change represented a structural shift, such that prices would remain low over the long run, then this may have led them to revise their beliefs about their total lifetime purchasing power. In that case, an increase in total spending would be consistent with almost any rational expectations model of consumer behavior. However, given that fuel represents just 5% of the average family’s total spending, the total lifetime purchasing power impact of even such a steep drop in fuel prices is relatively small. We examined a sample of 1 million core Chase customers to ascertain the magnitude of savings households experienced from lower gas prices, and whether and on what they spent these windfall gains; our findings are summarized in Fig. 6.8 (Farrell and Greig, 2016). We estimated that the 25% drop in gas prices generated a potential savings of $632 for middle-income households, of which only 42% went to durables purchases or savings. About three-fifths of the remaining 58% went to spending on nondurable goods and services other than fuel such as restaurants, retail, and groceries, while the rest went toward increased consumption of fuel. Some of this increase in fuel spending represented substitution away from other transit options.
FIGURE 6.8 Consumers spent 58% of their potential savings from lower fuel prices on nondurables.
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If the price change is certain to be short-lived, then a family seeking to smooth its consumption would save almost all of the dollars freed up by the price change in preparation for when prices returned to normal. If the price change is certain to be permanent, then they would be able to spend all of it. So, “should” families have spent 58% of these freed up dollars on nondurables? As it turns out, average fuel prices in 2017 were still at similar levels to 2015, suggesting that so far the decision to spend almost three-fifths of the freed up dollars may reflect about the right amount of optimism regarding the duration of the price change in this particular case. However, as we illustrate in the next example, consumer responses to price changes do not always work out so well.
2.4 Consumption, investment, and mortgage resets People increase their spending when they receive news that their adjustablerate mortgage (ARM) payment will reset downward, but then increase again when the reset actually starts. We examined a sample of 4321 deidentified US homeowners with a 5/1 ARM originated between April 2005 and December 2007, which reset to a lower interest rate between April 2010 and December 2012 (Farrell et al., 2017). Under Federal mortgage servicing rules, each of these homeowners was notified in advance of the adjustment what their new interest rate and monthly payment would be. When they received this information, they were also told that the new payment amount would last at least a year. We examined how these homeowners changed their credit card spending and revolving balances in response to the news of this predictable price change of known duration, as well as in response to the price change itself. Fig. 6.9 shows the pace of credit card spending around the change in these homeowners’ mortgage payments. Average spending increased sharply about 8 months before the rate reset and again about 2 months prior. This is consistent with a family responding to news that its lifetime purchasing power has increased slightly, as the cost of its housing will be lower for at least a year. Spending increased sharply again in the month when the rate actually reset, even though this second change was perfectly predictable. Looking across categories of spending (Fig. 6.10), we observed variation in the staging of the spending response. Spending on auto repair, transportation, home improvement, and services was highly responsive to news of the coming rate change and did not change much when rates actually changed, whereas spending on staples, healthcare, retail, and leisure was more responsive to the predictable change in payment amount than to news. With the notable exception of healthcare, it is likely that spending changes in response to the news had broader consequences for families’ well-being than changes in response to the actual cash flow event (for example, delaying an auto repair likely has broader impacts on a family’s ability to meet its needs than delaying a retail purchase).
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FIGURE 6.9 Credit card spending among consumers increased in response to information about a decline in their monthly mortgage payment and then increased again when the lower payment actually took effect.
FIGURE 6.10 Homeowners responded to news about a rate reset by increasing spending in some categories and to the rate reset itself by increasing spending in others.
152 Handbook of US Consumer Economics Cumulative average change in income, spending, and revolving balance
$9,327
$9,500
$928
$9,000
Financed on credit card
$8,500
$363 $565
Excess spending Excess financing
$8,000 $7,500
$8,399
$7,000
$8,964 $6,500
Likely paid for out of income
$6,000 $1,000 $500 $0 Income increase from mortgage reset
Total spending increase
Revolving balance increase
FIGURE 6.11 The credit card spending response to a mortgage rate reset exceeded the total decline in housing costs by 4%, but their credit card debt increased even more than was necessary to cover the excess.
Overall, as shown by the left column in Fig. 6.11, lower payments freed up about $8964 per year for the average homeowner over the year following the rate reset. However, as shown by the middle column, the annual increase in credit card spending exceeded those freed up dollars by 4%. Any understanding of consumer behavior that is built around a rational expectations framework would indicate that families should only increase consumption by more than the reduction in payments if they believed that their payments would continue to decline into the future. However, as the right column in Fig. 6.11 illustrates, the increase in revolving credit card balances was 2.6 times larger than the excess spending response. Simply by reallocating the dollars freed up by lower housing costs, these homeowners could have had exactly the same spending response with considerably lower credit card debt. Regardless of how optimistic they might have been about future interest rate adjustments, the average homeowner overreacted to the price change, increasing their consumption more than their long-run income increased. This is especially clear because for the vast majority of homeowners, any increase in expected lifetime purchasing power resulting from lower housing costs was only offsetting a decline in lifetime wealth from a lower home values. Median home values had declined by $84,000 in the period between when these mortgages were originated and when the payment resets occurred. These patterns suggest that the impact of a change in monthly housing payments on monthly nonhousing consumption exceeds their impact on lifetime purchasing power. These implications are further supported by later research on the impacts of mortgage modifications that were offered by the Home Affordable Modification Program (HAMP) in the wake of the Great Recession (Ganong and Noel, 2017; Farrell et al., 2017b). That research
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indicated that modifications which included a principal write-down were no more effective at reducing defaults than those that did not. These two classes of modifications had similar impacts on monthly payments, but very different impacts on total lifetime housing costs. Principal reduction that did not result in an incremental reduction in monthly mortgage payment had little impact on immediate cash flows and therefore little impact on default. Principal reduction also had no impact on consumption. Those who received payment and principal reduction spent no more than those who received only payment reductions, despite the wealth effect of principal reduction.
2.5 Income fluctuations around mortgage defaults The affordability of a mortgage is traditionally gauged by long-run metrics like the ratio of steady-state debt payments to expected average monthly income over the period of the loan.6 However, short-run fluctuations in income and prices are also of primary importance. This is evident from the fact that not only do people increase their spending when their mortgage payment drops but also people stop paying their mortgage when their income drops temporarily (Farrell et al., 2017b). Mortgage default closely followed a sharp drop in income, and mortgage payments recovered as income recovered (Fig. 6.12). This pattern held regardless of where homeowners fell in terms of traditional affordability metrics (e.g., payment to income ratio) or the amount of equity they had in their homes (e.g., “abovewater” borrowers, who have positive home equity in that they owe less on their home than their home is worth vs. “underwater” borrowers, who have negative home equity in that owe more on their home than their home is worth). The income drop before default was similar across all of these groups, suggesting that it was an income shock rather than a high payment burden or negative home equity that triggered default. Payment-focused mortgage debt reduction was equally effective at slowing default than principal-focused mortgage debt reduction precisely because it targeted relief to a household’s cash flow rather than to their balance sheet.
3. Conclusion Leveraging high-frequency, finely categorized data on income and expenditure of millions of households, we have described how expenditure responds to four specific changes in prices and income with varying degrees of impact and predictability. Consistent with the large body of evidence indicating limitations to consumption smoothing, we observe spending responses even to 6. For example, for a lender to get all of the regulatory and financial advantages associated with Qualified Mortgages, they must confirm that all of the borrower’s steady-state debt payments amount to no more than 43% of their expected average monthly income.
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FIGURE 6.12 On average, a substantial negative income shock preceded default for both belowmedian and above-median mortgage payment-to-income borrowers and both abovewater and underwater borrowers.
changes that likely had no impact on lifetime purchasing power. We are able to go beyond confirming the general principle that consumption smoothing is limited, showing that expenditure responds to both information about a future price or income change and the (temporary) change in purchasing power when it actually occurs. Furthermore, we find that expenditures for which timing is likely to have welfare implications, such has healthcare, adjust upward in response to positive news and downward in response to negative events. These findings indicate consumption patterns that violate not only the most stylized representation of the PIH but also contemporary adaptations built on a rational expectations framework. At the same time, the evidence indicates that consumers do not simply live hand to mouth; in two cases (mortgage rate resets and job separations), we observed consumers reacting to news about changes to lifetime purchasing power even before those changes took effect. This suggests that consumers do manage their cash flow and take the future into account when making consumption decisions. Understanding the nature
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and extent of limitations to consumption smoothing is crucial for the design of optimal policies and financial product offerings. We offer a few specific examples below. Consumers need tools that make it easier to budget, save, and adjust. In response to perfectly forecastable changes in their long-run purchasing power, we have observed consumers underreacting to the prospect of a decline (expiration of UI benefits) and overreacting to an improvement (mortgage rate resets). The cognitive burden associated with reprogramming a family’s spending, saving, and borrowing when circumstances change is almost never taken into account when consumption behavior is analyzed through the lens of a rational expectations framework. Many families would likely benefit from financial tools that help with the complicated process of making these adjustments. These tools would make budgeting more intuitive and make it easier to stick to a plan with “set-it-and-forget-it” (or “reset-it-and-forget-it”) functionality. For example, if more UI recipients were given a tool to preallocate a fraction of their benefits to a sidecar account to provision for the day they exhaust their eligibility, we may not have observed the sharp declines in grocery spending. In addition, many might benefit from simpler financial offerings that make it less likely these kinds of adjustments will be necessary in the first place. Families need more shock absorbers, especially for healthcare. In three of our four examplesdtax refunds, mortgage resets, and UI expirationdwe observe predictable changes in cash flow having sharp and significant impacts on many important expenditure categories, including healthcare. Our research has also consistently shown that these impacts are smaller for families with higher levels of cash savings. It should be easier for families to save for emergencies. During tax filing season, families appear to look to their refunds to access a large cash infusion, but emergencies happen throughout the year. Health savings accounts, medical reimbursement accounts, and other taxadvantaged savings vehicles make it possible for families to provision specifically for health emergencies, but they carry complicated rules and impose significant penalties if the family ends up needing the cash for a nonhealth emergency. Policies and tools that enable families to provision more effectively might include many of the useful features of these existing tools, but without these rigidities. Means testing for social safety nets should focus on income levels, not just asset levels. Asset tests penalize families for building up reserves necessary to smooth consumption. The impact of even short-run changes in purchasing power on current consumption exceeds their impact on lifetime purchasing power. Families who have provisioned for emergencies should not be forced to exhaust those savings before they can get help with managing the impacts of volatility. Underwriting standards should account for income volatility, not merely steady-state income. Static affordability metrics like the ratio of steady-state
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debt payments to expected average monthly income are not good predictors of default risk, in part because even transitory disruptions to income have larger impacts than would be predicted by any rational expectations model. Dynamic approaches which account for cash flow dynamics and forecast income and expense risk should be more widely integrated into underwriting practices. There is value in tracking even transitory fluctuations in income and spending. For most economic monitoring applications, income and spending measures are adjusted to smooth out predictable variation (for example, seasonal variation). These adjustments are implicitly motivated by assumptions about the welfare impacts of predictable fluctuations. The findings we report here suggest that those assumptions should be revisited for some applications. In general, the justification for smoothing should be more explicit and question-specific. We need a better theoretical framework for understanding consumer behavior. Many of the patterns we report here, and other patterns that have been consistently established in academic studies, are inconsistent with the workhorse rational expectations framework that has helped to organize thinking around consumer behavior. Some scholars have attributed a lack of consumption smoothing to liquidity constraints, but these models would fail to explain the excess consumption we observed, for example, in the consumption response leading up to and after mortgage resets (Carroll, 1997; Kaplan et al., 2014). Behavioral scientists have offered alternative frameworks that relax assumptions of rational expectations and intertemporal optimization, but none are as comprehensive or broadly applicable (for example, Madrian and Shea, 2001; DellaVigna and Malmendier, 2006; Olafsson and Pagel, 2018). Key features of some of these models include more robust conceptions of how consumers form and update expectations about the future, and how they approach intertemporal trade-offs. Many of these features have been used to extend the constrained optimization paradigm that lies at the foundation of modern microeconomics, but these extensions are often very applicationspecific. The design of optimal public policies and financial product offerings would benefit immeasurably from a coherent model of consumer behavior that captures these nuances. Products and policies designed to serve perfectly forward-looking consumers, or entirely myopic hand-to-mouth consumers, will always be of limited use to most real-world consumers.
Appendix FIGURE 6.13. Description of sampling criteria and data asset for each study.
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Event
Sampling criteria and data asset
Arrival of tax refund (Farrell et al., 2018a, 2018b) Figs. 6.2e6.4
This report draws on the JPMC Institute healthcare out-of-pocket spending Panel (JPMCI HOSP) data asset and examines how healthcare payments vary in the days and weeks around when account holders receive their tax refunds. We analyze average outof-pocket healthcare expenditure on over a dozen categories of healthcare goods and services for each day in the 100 days before and after a tax refund payment, for 1.2 million checking account holders in the JMPCI HOSP who received a tax refund between 2014 and 2016. The JPMCI HOSP data asset was constructed using a sample of deidentified core Chase customers for whom we observe financial attributes, including out-of-pocket healthcare spending between 2013 and 2016. For the purposes of our research, the unit of analysis was the primary account holder. We focused on accounts held by adults aged 18e64, as adults 65 and older were more likely to make payments using paper checks, which we could not categorize. To provide better visibility into income and spending, we selected accounts which met the following criteria: 1. Had at least five checking account outflows each month 2. Had at least $5000 in take-home income each year 3. Used paper checks, cash, and non-Chase credit cards for less than 50% of their total spending. The JPMCI HOSP data asset includes customers who resided within the 23 states in which JPMorgan Chase has a retail branch presence. We reweighted our population to reflect the joint age and income distribution among the 18e64-year-old population within each state.
Job loss (Farrell et al., 2016) Figs. 6.5e6.7
From a universe of 28 million Chase checking account holders, this report assembled an anonymized sample of 160,000 families across 18 states who met the following five criteria. 1. Received direct deposit of their first unemployment insurance (UI) check after December 2013 and their last UI check before June 2015 2. Received UI for six or fewer contiguous months 3. Experienced one spell of receiving UI benefits 4. Live in states that offer 26 weeks of UI benefits. In finding 3, we compare this group to UI recipients in Florida where benefits lasted 16 weeks in 2014 and 14 weeks in 2015 5. Have at least five outflows out of their checking account in the 3 months before and after UI receipt. Among our sample, median earnings among families that received UI benefits was $4,540, roughly comparable with the national median of $5106. Among the families in our sample, we studied income and spending by analyzing inflows and outflows out of the checking account as well as on Chase debit and credit cards. We defined income as all inflows which are not explicitly categorized as transfers from other financial accounts, and we rescaled take-home labor income into pretax dollars. We defined spending to include debit card expenditures, Chase credit card expenditures, consumer debt payments (mortgages, auto loans, non-Chase credit cards, and student loans), bills (e.g., electricity, cable, insurance), and cash withdrawals from the ATM.
Continued
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dcont’d Event
Sampling criteria and data asset
Gas price decline (Farrell and Greig, 2016) Fig. 6.8
For this report we rely on JPMorgan Chase anonymized data on consumer clients who are primary account holders. To avoid double counting of financial activity, all joint accounts are captured under the primary account holder. From a universe of over 28 million anonymized checking account holders, we created a sample of approximately one million debit card holders who meet the following sample criteria: 1. They have a checking account and at least five outflow transactions from their checking account per month between October 2012 and January 2016. 2. They do not hold a gas station specific card. 3. They live in a zip code with at least 140 households in our sample. 4. They live in a metro area with at least 5 zip codes and at least 750 households in our sample.These criteria give us confidence that we are focusing on core Chase clients and have sufficient coverage of the geographic areas in which we assess the impact of low gas prices on spending behavior. These criteria constrain our sample to the 23 states with Chase branch locations. The demographic characteristics of this sample are slightly different from the nation in that the sample overrepresents primary account holders between 25 and 54 years old, men, households in the West, and households with higher incomes compared to the US population.
Mortgage resets (Farrell et al., 2017) Figs. 6.9e6.11
From a universe of over 6 million mortgage customers, we created a sample of 4321 homeowners who met the following criteria: 1. Had one 30-year 5/1 adjustable-rate mortgage (ARM) originated between April 2005 and December 2007 that reset to a lower rate between April 2010 and December 2012 2. Had not modified or refinanced their mortgage before reset 3. Made interest-only or interest plus principal payments To connect the impact of ARM resets to changes in spending, we then filter our sample to include those customers who have a Chase credit card that 4. Was active at least 24 months before the reset date of their ARM and 5. Had a median of at least 10 transactions per month in the 24 month window surrounding the reset date of their ARM. We require the Chase credit card to be active at least 24 months before the reset date of their ARM to eliminate households who opened a credit card just before reset to make a large purchase. We also require a median of at least 10 transactions per month in the 24month window surrounding the ARM reset date to eliminate households whose Chase credit card is not sufficiently active to be representative of their consumption.
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dcont’d Event
Sampling criteria and data asset For our sample, we observe loan amount, term, interest rate, monthly payment, home value estimate, monthly credit card spending, spending by category, revolving balance, and credit limit. We also have access to demographic information such as customer age and annual income. Our sample is not perfectly representative of the typical household with any type of mortgage. Our loan amounts and LTVs are in line with the Federal Housing Finance Agency benchmarks, while our mortgage rates are somewhat lower. The sample also exhibits higher income levels than Survey of Consumer Finance benchmarks. This is partially the result of studying hybrid ARMs. The income of our sample also increases as we screen for credit card holders and sufficient credit card activity.
Income fluctuations around mortgage default (Farrell et al, 2018a,b) Fig. 6.12
For this analysis, we included homeowners who met the following criteria: 1. Chase customers with only one mortgage and a Chase deposit account 2. First default date between October 2013 and October 2014 3. Had monthly mortgage data and an active deposit account for at least 12 months before and 12 months after default. We used this sample of 10,815 mortgages to analyze the correlation between income and default. We then split this sample into aboveand underwater borrowers and analyzed each subsample separately. To examine the correlation between income and default split by above- and below-median premodification mortgage payment to income, we used the sample above but added a restriction that the borrower must have received a modification in the 12 months before or after default. This limitation is necessary because we sourced borrower income from their modification application. This generated a sample of 1807 mortgages.
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160 Handbook of US Consumer Economics Farrell, D., Greig, F., Hamoudi, A., 2018a. Deferred Care: How Tax Refunds Enable Healthcare Spending. JPMorgan Chase Institute. Farrell, D., Greig, F., Hamoudi, A., 2018b. Filing Taxes Early, Getting Healthcare Late: Insights from 1.2 Million Households. JPMorgan Chase Institute. Farrell, D., Bhagat, K., Narasiman, V., 2017a. The Consumer Spending Response to Mortgage Resets: Microdata on Monetary Policy. JPMorgan Chase Institute. Farrell, D., Bhagat, K., Ganong, P., Noel, P., 2017b. Mortgage Modifications After the Great Recession: New Evidence and Implications for Policy. JPMorgan Chase Institute. Farrell, D., Ganong, P., Greig, F., Noel, P., 2016. Recovering from Job Loss: The Role of Unemployment Insurance. JPMorgan Chase Institute. Ganong, P., Noel, P., 2017. The Effect of Debt on Default and Consumption: Evidence from Housing Policy in the Great Recession. Mimeo, Harvard University. Available at: https:// scholar.harvard.edu/ganong/publications/effect-debt-default-and-consumption-evidencehousing-policy-great-recession. Ganong, P., Noel, P., 2018. Consumer Spending During Unemployment: Positive and Normative Implications. Mimeo, Harvard University. Available at: https://scholar.harvard.edu/ganong/ publications/how-does-unemployment-affect-consumer-spending-job-market-paper. Gelman, M., Kariv, S., Shapiro, M.D., Silverman, D., Tadelis, S., 2015. How Individuals Smooth Spending: Evidence from the 2013 Government Shutdown Using Account Data. No. w21025. National Bureau of Economic Research. Hall, R.E., 1978. Stochastic implication of the life cycle-permanent income hypothesis: theory and evidence. Journal of Political Economy 86, 971e988. Internal Revenue Service (IRS). Filing Season Statistics for Week Ending, 2016. Internal Revenue Service, 2017. Available at: https://www.irs.gov/newsroom/filing-season-statistics-for-theweek-ending-december-30-2016. Jappelli, T., Pistaferri, L., 2017. The Economics of Consumption: Theory and Evidence. Oxford University Press. Kaplan, G., Violante, G.L., Weidner, J., Spring 2014. The wealthy hand-to-mouth. In: Brookings Papers on Economic Activity. Madrian, B.C., Shea, D.F., 2001. The power of suggestion: inertia in 401(k) participation and savings behavior. Quarterly Journal of Economics 116 (4), 1149e1187. Olafsson, A., Pagel, M., 2018. The liquid hand-to-mouth: evidence from personal finance management software. The Review of Financial Studies 31 (11), 4398.